Abstract
Google Ads is a powerful tool for companies wishing to gain visibility on Google searches, as it offers impression privileges for advertisers, by featuring the ad above the organic results listing. This chapter contributes to the optimization of Google Ads campaigns. It includes an empirical study with a sample of marketing professionals exploring their views on the challenges of Google Ads as a digital marketing tool. According to the participants in this study, Google Ads campaign profitability depends, largely, on the ability to choose a keyword pool that achieves the company's goals. Moreover, the complexity of these pay-per-click decisions, the costs involved, and its business implications demand more reasoned, quantified, and, if possible, optimized solutions. The chapter develops linear programming optimization models based on impressions, clicks, conversions, and billing. The models are tested on a real example using Excel optimization add-ins.
TopBackground
The Web has turned into one of the major world markets for the acquisition of goods and services. With an average growth of 113 million a year, the number of digital buyers was 1.66 billion in 2017. It is estimated that by 2021 they may reach 2.14 billion (Statista, 2019a). Digital buyers are not only very numerous but also spend a lot on money in online sales. Online turnover has been growing at a rate of 291 billion euros per year. Sales were 2,074 billion in 2017 and are expected to more than double in coming years, reaching 4,390 billion euros in 2021 (Statista, 2019b).
The rapid evolution of the digital society and buying experiences forced companies to a massive online presence, confronting them with new technological and social dynamics (DeCastro, de Bittencourt, Chaves, Barreiro, & Reis, 2015). At the global level, new digital markets have flourished, immersed in an increasing complexity and in the abundance of information. In this scenario, business decision making becomes more difficult and diffuse, requiring new methodologies and a constant updating by professionals and companies (Miranda & de Oliveira Arruda, 2004). Despite this, the evolution of communication and of new forms of business is unstoppable, creating stimulating interactions, and definitely bringing sellers and buyers closer together (Gielens & Steenkamp, 2019).
Not surprisingly, the biggest e-commerce companies take on gigantic proportions. Amazon leads with a turnover of 96.3 billion euros per year and Google ranks second with revenue of 67.5 billion euros (Dunlop, 2019). They are enthusiastic metrics that augur a promising future for this new form of business.
Key Terms in this Chapter
Conversion: Conversion refers to a desired action performed by a consumer as a reaction to an advertisement or other marketing effort. The desired action can take many forms including the purchases, membership registrations, newsletter subscriptions, and application downloads.
SEO: Search engine optimization (SEO) is the process of increasing the quality and quantity of website traffic by increasing the visibility of a website or a web page to users of a web search engine. SEO refers to the improvement of unpaid results (known as “natural” or “organic” results) and excludes direct traffic/visitors and the purchase of paid placement.
Ad Targeting: Ad targeting refers to the selection of potential customer groups to which an advertisement will be displayed. This specification of the ad’s audience is done using targeting parameters including demographic and geographic information, interests, and device preferences.
Search Engine: A web search engine or Internet search engine is a software system that is designed to carry out web search (Internet search), which means to search the World Wide Web in a systematic way for particular information specified in a textual web search query.
Ad Placement: Ad placement include all advertising spaces, mostly paid, offered by online publishers, websites, and social networks to advertisers to display their advertisements. The individual placements have different potential for reaching the users and perform differently when it comes to the type of content chosen for the advertisement.
Analytics: Consumer behavioral data related to their platform usage behaviors such as the number of visits to a platform, time spent on the platform, and actions when using the platform. Using these behavioral data, advertisers and marketers are able to segment target audience to increase their experiences and to improve campaign effectiveness.
Digital Advertising: Used interchangeable with Internet or online advertising. This term covers a wide variety of online advertising formats, ranging from email, social media applications, search engine advertising, mobile advertising, video advertising, etc.
Big Data: A term that defines a large dataset that produces in the size of collected data over time. It refers to the size of dataset that surpasses the capturing, storage, management, and analysis of traditional databases. The term refers to the dataset that has huge, more diverse, and multifaceted structure, accompanies by difficulties of data storage, analysis, and visualization. Big Data are characterized with the commonly known attributes: high-volume, -velocity, and –variety information assets.